Search Results (96)

This study examines the impact of asymmetric oil price fluctuations on inflation in Morocco over the period 1998Q1–2022Q4. Given the country’s dependence on imported energy, it employs the Nonlinear Autoregressive Distributed Lag (NARDL) model to investigate the short- and long-run asymmetric effects of oil price shocks. The results reveal that positive oil price shocks significantly increase inflation in the short run, whereas their effects weaken in the long run. The findings also indicate that oil price increases generate stronger inflationary pressures than oil price decreases. Furthermore, the exchange rate acts as an indirect transmission channel with a limited and lagged effect. These results highlight the importance of strengthening energy resilience and the macroeconomic framework to mitigate inflationary pressure. Full article

This article investigates the factors that determine the dynamics of mortgage lending in Bulgaria and the Euro area by using ordinary least squares (OLS) regression models based on stationary time series over the period 2010–2025. The results show that in Bulgaria, the dynamics of mortgage lending are determined primarily by wage growth, inflation, and the high liquidity of the banking system, which increases banks’ capacity to extend new loans. In contrast, in the Euro area, the main factor driving mortgage lending trends is interest rates on mortgage loans, with the development of the real estate market, as measured by house price index, also exerting a significant influence. The findings further indicate that, despite the high degree of economic integration between Bulgaria and the European Union, the factors determining mortgage lending differ, which justifies the need for separate modeling of mortgage loans in the two economies. Moreover, mortgage lending transmission mechanisms differ substantially across the two economies despite their close monetary integration, highlighting the importance of country-specific institutional characteristics. The faster growth of mortgage lending by Bulgarian banks compared to those in the Euro area does not yet pose risks to the stability of Bulgaria’s banking system. Full article

Studies on the implications of digital financial development and their impact on monetary policy effectiveness have shown mixed results, with some evidence of interdependence and potential transmission challenges. While some studies highlight positive growth impacts moderated by institutions with risks to policy sovereignty from innovations, research on South Africa remains minimal, despite having a mature financial system and rapid uptake of digital finance. The study used an ARDL model with the digital financial development index, inflation, interest rate, financial deepening, and an interaction of the digital financial development index and interest rate to analyse the implications of digital finance and its impact on monetary policy effectiveness in South Africa. Utilising annual data from 1990 to 2024, the results showed that the digital financial development index positively influences inflation in the long run. In addition, interest rates have a significant negative impact on inflation in the long run, while financial deepening, exchange rate and GDP per capita are insignificant in the long run. Furthermore, interest rate interactions with the digital financial development index exert downward inflationary pressure, but the interaction term is statistically significant in the long run. Therefore, digital financial development mitigates the inflationary effect of interest rates and appear to strengthen the monetary policy effectiveness in controlling inflation in the long run. We recommend that policymakers consider incorporating digital financial development indicators into the model for determining interest rates when developing a strategy to control inflation, thereby enhancing monetary policy effectiveness. Full article

This paper analyses the short-run dynamic relationship between monetary policy and banking market structure in Colombia during a period of post-pandemic inflation and aggressive policy tightening. Using monthly credit portfolio data for 2017–2024, we compute several concentration indicators (the Herfindahl–Hirschman Index (HHI), CRk ratios, and a dominance index) and employ three complementary identification strategies to evaluate the causal effect of monetary policy innovations on banking concentration. First, a structural VAR model identified through sign restrictions finds that contractionary shocks are associated with a short-run increase in banking concentration (median peak response: +0.60 HHI points at h = 3; 90% credible set: [+0.12, +1.16]), contrasting with the negative short-run response obtained under recursive reduced-form identification. Second, an extended VAR including credit portfolio growth as a mechanism variable confirms that contractionary shocks compress aggregate lending but do not generate robust, persistent changes in concentration. Third, local projections with regime-interaction terms formally test the nonlinear mechanisms discussed in the literature and find evidence of state-dependent transmission: the concentration response is larger in the low-inflation regime and attenuates during high-inflation episodes. All estimated effects are transitory and horizon-sensitive, reinforcing a cautious interpretation. The paper contributes new evidence from an emerging economy on the structural consequences of monetary policy and highlights the importance of identification assumptions in determining the direction of this effect. Full article

Energy price shocks have been the main drivers of inflation in Europe during the last decade. This paper examines cross-country differences in the transmission and magnitude of energy supply shocks in the Visegrád Group countries (Poland, Czechia, Hungary, and Slovakia) over the period 2015–2026. To identify energy-related disturbances and evaluate their macroeconomic effects, we estimate Bayesian Structural Vector Autoregressive (BSVAR) models with sign restrictions and complement the analysis with Local Projections. The results indicate substantial heterogeneity in inflation responses across countries. Oil supply shocks increase inflation by approximately 0.8 percentage points in Czechia and 0.7 percentage points in Hungary, compared with around 0.5 percentage points in Poland and Slovakia. Similar response patterns are observed for gas supply shocks, suggesting that stronger commodity price pass-through contributes to the larger inflationary effects in Czechia and Hungary. The analysis also reveals methodological challenges in identifying the effects of increased LNG imports. Both models indicate LNG-related shocks are highly correlated with pipeline gas supply shocks. In fact, its imports have mitigated energy shortages and reduced price pressures. Full article

This paper investigates how restrictive monetary policy impacts long-term strategic infrastructure financing under conditions of geopolitical fragmentation and supply-side inflationary pressures. The analysis focuses on Poland, a medium-sized European economy exposed to energy transition requirements, post-pandemic supply chain disruptions, and regional geopolitical instability. Using quarterly data for 2005Q1–2024Q2, the study employs a Structural Vector Autoregression (SVAR) model to evaluate the transmission of monetary policy shocks to inflation dynamics and sectoral credit allocation. The research examines bank lending directed toward infrastructure-intensive sectors, including energy, utilities, transport, and strategic industrial investments. Empirical results demonstrate that while monetary tightening contributes to a statistically significant reduction in inflationary pressures over the medium term, it simultaneously triggers unintended consequences. Specifically, higher policy rates are associated with a persistent contraction in long-term infrastructure-related credit volumes. Impulse response analysis reveals that this decline in strategic infrastructure financing is disproportionately stronger and more enduring than the drop observed in aggregate corporate lending. These findings highlight asymmetric monetary transmission effects across investment categories. Consequently, the paper suggests implementing targeted macroprudential and liquidity-support instruments to protect strategic sectors without compromising inflation stabilization objectives. Full article

This paper examines whether prolonged sanctions and major geopolitical episodes associated with financial fragmentation alter exchange-rate dynamics and weaken the explanatory power of domestic macroeconomic channels and strengthen external financial dominance of traditional exchange-rate transmission mechanisms. Standard exchange rate theories are based on the concepts of Purchasing Power Parity (PPP) and Uncovered Interest Parity (UIP). However, the application of continuous sanctions could weaken this explanatory power and change exchange rate dynamics. The present study applies a combined framework of Autoregressive Distributed Lag (ARDL), Error Correction Modeling (ECM), Vector Autoregression (VAR) and structural break analysis to study the exchange-rate behavior in response to repeated geopolitical shocks using monthly data for Russia from 2005 to 2025. The results indicate that external variables such as the US dollar index and oil prices are important determinants of exchange rates, while inflation and interest rate differentials associated with PPP and UIP have little explanatory power. Structural break tests detect major regime shifts associated with the Global Financial Crisis, Crimea-related sanctions episode, COVID-19 pandemic and Russia–Ukraine conflict. The error correction process indicates that the speed of adjustment to equilibrium is slow, which means that traditional exchange-rate relationships will continue to diverge. In general, the results suggest a regime-dependent exchange rate environment in which external financial factors tend to dominate domestic adjustment mechanisms. Our study contributes to the literature on exchange rates, sanctions and financial fragmentation by providing evidence on how geopolitical shocks shift the relative importance of domestic and external determinants in a highly sanctioned economy. Full article

Regression methods are widely used to investigate macroeconomic relationships; however, they are generally estimated without first examining whether the underlying variables exhibit fractal structures, persistence, and chaotic dynamics. Although nonlinear regression models relax the assumption of linearity, they rarely account for the complex geometric, long-memory, and dynamical properties that characterize macroeconomic time series. Motivated by this limitation, this study proposes a fractal-oriented Lie regression framework that integrates fractional persistence and Lie algebra to model nonlinear macroeconomic interactions within a unified analytical structure. For Türkiye, the empirical analysis employs monthly data on inflation, interest rates, exchange rates and oil prices covering the period 2000M1–2026M1, encompassing major economic crises and structural breaks. Prior to model estimation, the dynamical characteristics of the variables are examined using entropy measures, long-range dependency analysis, Lyapunov exponents and attractors. The results reveal persistent fractal structures, significant fractional dependence and chaotic behavior, indicating that macroeconomic variables evolve within a complex nonlinear dynamical system rather than around a conventional equilibrium. Based on these results, the variables are represented within a Lie algebra framework in which nonlinear transformation matrices preserve the underlying geometric structure while simultaneously capturing both self-dynamics and cross-variable interactions. The proposed Lie regression model demonstrates substantial improvements over standard regression methods in both model adequacy and forecasting performance. Oil prices emerge as the dominant transmitter of shocks by generating pronounced asymmetric effects on inflation, exchange rates and overall macroeconomic stability. The model achieves remarkable forecasting accuracy by reducing RMSE, MAE, and MAPE from 18.58, 13.61, and 69.92 under a standard regression model to 0.27, 0.22 and 16.4, respectively. Finally, the estimated Lie transformation matrix is employed as a policy-simulation mechanism to evaluate the transmission of alternative oil-price shocks. Scenarios based on 5%, 10%, and 20% increases in oil prices quantify the resulting adjustments in inflation, interest rates, and exchange rates by providing forward-looking assessments of macroeconomic vulnerability. The proposed framework extends standard regression analysis by explicitly incorporating fractional persistence and chaotic dynamics into a Lie algebra representation, thereby offering a more accurate and theoretically consistent approach for modeling complex macroeconomic systems. Full article

This paper provides a comparative, descriptive assessment of macroeconomic adjustment dynamics within the euro area in response to the inflationary and crisis-related disturbances of the 2015Q1–2024Q4 period, with particular attention to the COVID-19 pandemic and the geopolitical shock associated with the Russia–Ukraine conflict. Using a Vector Error Correction Model (VECM) framework estimated separately for the euro-area aggregate and four representative core and peripheral economies (Germany, France, Spain, and Greece), the analysis characterizes long-run equilibrium relationships and short-run adjustment dynamics among output, inflation, public debt, and unemployment. Rather than identifying structurally causal transmission, the study interprets the estimated cointegration structures, error-correction speeds, and impulse-response patterns as reduced-form indicators of how differently national systems absorb common disturbances. The comparative evidence points to substantial heterogeneity: core economies display comparatively more contained and coordinated adjustment, whereas peripheral economies exhibit stronger fiscal sensitivity, more persistent labor-market adjustment, and greater macroeconomic interdependence. Read together, these patterns suggest that resilience within the monetary union is better understood not solely as equilibrium restoration, but as the persistence, coordination, and stability of the broader adjustment process—and that asymmetric adjustment structures persist despite a common monetary framework. Full article

Macroeconomic systems are increasingly characterized by fractal structures, entropy-generating processes, and chaotic dynamics that challenge the assumptions of traditional regression methods. The presence of self-similarity, fractal structure, and sensitivity to initial conditions suggests that macroeconomic variables evolve through complex interactions that cannot be adequately explained within an equilibrium-based method. Motivated by this perspective, this paper tested the relationships between oil prices and macroeconomic variables in the United States over the period of 1960–2024 using a suggested fractal regression approach. The analysis proceeds in two stages. In the first stage, fractal, entropy, and chaotic structures of the variables were analyzed by employing entropy measures, Lyapunov exponents, attractor diagnostics by including Lorenz and Julia structures, and tests for fractal dimension: d parameter (GPH) and d parameter (Phillips), and long range dependendeceLo’s Modified R/S, and Hurst–Mandelbrot R/S. Our results explored evidence of fractal structure, complexity, and chaotic behavior within the selected macroeconomic series by indicating the presence of nonlinear dynamics and sensitivity to initial conditions. In the second stage, a proposed chaotic–fractal-based regression model is employed to explore the transmission mechanism of oil price to economic growth, inflation, and unemployment. By directly incorporating Lyapunov and fractal-based measures into the regression method, the model captured nonlinear interactions that are overlooked by traditional methods. The results revealed that oil price shocks generate chaotic and fractal effects across macroeconomic variables and that these effects vary according to the degree of chaotic divergence embedded in the system. Overall, the results suggested the interconnected roles of fractality, entropy, and chaos in shaping macroeconomic dynamics and showed the importance of chaos- and fractal-based modeling methods for understanding the economic consequences of energy shocks and their policy implications. Full article

Inflation expectations are central to monetary policy transmission, yet relatively little is known about whether individuals’ own investment experiences are associated with how they form such expectations across different forecast horizons. This study examines the association between self-reported past investment performance and horizon-specific expected cumulative consumer price changes at the one-, three-, and five-year horizons using a large-scale online survey of 157,523 active Japanese investors. Because the survey asks respondents how consumer prices will change over each horizon, the three- and five-year responses are interpreted as expected cumulative price changes rather than annualized inflation rates. Ordered probit models are estimated while controlling for demographic, socioeconomic, and behavioral characteristics. The results show a horizon-dependent conditional association: self-reported investment performance is not significantly associated with one-year expectations in the full specification, whereas it is positively and significantly associated with three- and five-year expectations. Formal stacked OLS interaction tests indicate that the association differs significantly across horizons, and additional threshold-specific probit models show that the pattern is most evident for moderate inflation-expectation thresholds. The economic magnitudes are statistically precise but modest. Heterogeneity analyses further suggest that the association is weaker among respondents with higher financial literacy and higher assets, and stronger among respondents with a more myopic view of the future. Because the analysis relies on cross-sectional observational data and subjective performance measures, the findings should be interpreted as conditional associations rather than causal effects. Full article

Remote tele-rehabilitation requires objective pain assessment, but existing approaches fail in two distinct ways. Self-report scales such as the Visual Analog Scale and the Numeric Pain Rating Scale are easy to falsify, opening a special case of the Oracle problem in blockchain-based insurance. Cloud-based computer vision handles falsification but transmits raw biometric video off the patient’s device, violating privacy requirements. A decentralized Edge AI-Oracle architecture is proposed that combines MediaPipe Face Mesh landmark extraction with a recurrent classifier mapping Action-Unit feature sequences to a learned pain score aligned with the Prkachin and Solomon Pain Intensity scale. The recurrent cell is selected empirically across short-context (T = 2) and long-context (T = 120 frames at 24 fps) regimes, with a two-layer Long Short-Term Memory (LSTM) network adopted for deployment. Inference and Elliptic Curve Digital Signature Algorithm (ECDSA) signing run inside an ARM TrustZone Trusted Execution Environment (TEE). Biometric logs are stored off-chain on the InterPlanetary File System (IPFS). Smart contracts anchor results on-chain and open a 24 h optimistic verification window for an off-chain Watchtower auditor. On SynPAIN the LSTM reaches F1 = 0.683 on T = 120 video (leave-one-stratum-out), with a directional but non-significant advantage over Gated Recurrent Unit (GRU) (Wilcoxon p = 0.167). Cross-dataset validation on BioVid Heat Pain Database Part A (87 subjects, 174 paired observations, leave-one-subject-out) yields F1 = 0.519 for LSTM and 0.499 for GRU (Wilcoxon p = 0.549). A processor-only TEE surrogate benchmark estimates 1.96 ms (FP32) and 0.45 ms (INT8) inference latency at T = 120 with a 0.34 MB footprint and 707 µs ECDSA signing latency, leaving the INT8 inference latency more than an order of magnitude below the 33 ms per-frame budget. The dual-layer storage reduces gas costs by a factor of 23.4 (160,261 vs. 3,744,872 gas), corresponding to an illustrative mainnet cost of approximately 0.53 USD per submission at 1 gwei, rising to roughly 16 USD at a busier 30 gwei, and falling to approximately 0.005 USD on Arbitrum One (April 2026 reference parameters), so that continuous monitoring is economically practical on Layer-2. An adaptive-adversary analysis of the Watchtower shows that gross score tampering is detected at every usable operating threshold, whereas a rational adversary who inflates by less than the dispute threshold, or who shapes the injected score to fall just inside it, evades detection. Because the false-positive rate reaches zero only for $\mathsf{\delta }\gtrsim 0.15$, the protocol bounds rather than eliminates patient-side fraud and motivates a zero-knowledge proof-of-inference successor. The framework is architecturally and economically feasible as a cryptographically verifiable, privacy-preserving tele-rehabilitation substrate aligned with General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) requirements through the Zero-Video Transmission principle, while remaining economically viable under post-Dencun mainnet and Layer-2 conditions. Recognition accuracy on real-world data and robustness to small-magnitude tampering remain limitations that the interchangeable recognition and audit components must improve before clinical deployment. Full article

This study aims to identify and prioritize digital economy factors affecting inflation and to determine effective policy strategies for managing digitally driven inflationary pressures in the context of financial systems and risk dynamics. The analysis considers twelve key digital economy indicators, including e-commerce penetration, digital payment systems, internet infrastructure, price transparency, digital advertising, Industry 4.0 technologies, data-driven inventory and demand systems, fintech adoption, cryptocurrency usage, and digital financial access. In parallel, eight policy strategies are evaluated, covering digital price transparency, expansion of digital payments, digital logistics optimization, digital public services, smart manufacturing, intelligent-based demand forecasting, fintech integration, and digital workforce development. The study employs a novel intelligent-supported decision-making framework integrating an attention-based expert weighting approach, generalized fractal fuzzy sets, the MEREC method, and the ARLON technique. The empirical design is based on expert evaluations obtained from ten specialists with at least 12 years of experience in digital economy, finance, and policymaking. Rather than relying on country-specific or time-series inflation datasets, the study examines the structural relationship between digitalization and inflation through a multi-criteria expert-based approach, with data collected in 2025. The findings indicate that e-commerce penetration and the prevalence of digital payment systems are the most influential factors affecting inflation. In addition, digital price transparency and the expansion of digital payment systems emerge as the most effective strategies for mitigating inflationary pressures. These results provide important insights into how digital transformation reshapes inflation dynamics, monetary transmission mechanisms, and inflation-related financial risks. The proposed model offers a robust and systematic framework for analyzing inflation in digitalized economies and supports policymakers and financial decision-makers in managing emerging risks in intelligent-driven economic environments. Full article

Background: HIV-HBV co-infection remains a major public health challenge, particularly in sub-Saharan Africa. HBV co-infection worsens clinical outcomes among people living with HIV by accelerating liver disease and complicating treatment. Although antiretroviral therapy can effectively suppress both viruses, achieving optimal HIV viral suppression remains critical for reducing morbidity and transmission. While several factors influencing viral suppression among PLHIV are well documented, evidence on HIV viral suppression among HIV-HBV co-infected individuals is limited, especially in resource-limited settings like Ethiopia. Furthermore, data on the magnitude of viral suppression and its associated factors in this population are scarce. Therefore, this study aimed to assess the magnitude of HIV viral suppression and identify its associated factors among adult HIV-HBV co-infected patients in Northwest Ethiopia. Objective: This study aimed to assess the magnitude and factors associated with HIV viral suppression among adult people living with HIV-HBV Co-infection in Northwest Ethiopia. Methods: An institution-based cross-sectional study was conducted in Northwest Ethiopia among adults with HIV-HBV co-infection on antiretroviral therapy. A simple random sample of 402 participants was selected. Data were collected using a pretested structured interviewer-administered questionnaire and medical record review, covering sociodemographic, clinical, behavioral, treatment, follow-up, and adherence factors. HIV viral suppression was defined as a plasma viral load < 1000 copies/mL. Data were coded in EpiData 4.6 and analyzed using STATA 18. Descriptive statistics estimated suppression rates. Bivariable and multivariable logistic regression identified factors associated with suppression; variables with p < 0.25 in bivariable analysis were included in the multivariable model. Statistical significance was set at p < 0.05 with adjusted odds ratios and 95% confidence intervals reported. Model fit was assessed using the Hosmer–Lemeshow test, and multicollinearity was checked using variance inflation factors. Results: There were 423 participants in all. Among the 423 HIV-HBV co-infected adults on antiretroviral therapy included in this study, 138 (34, CI, 30–39%) achieved HIV viral suppression, while 264 (66%) had an unsuppressed viral load at the time of assessment. Viral suppression was found to be independently correlated with the ART TDF-3TC-LPV/r regimen, first-line medication adherence, bedridden functional level, missed clinic appointments, and length of therapy. While TDF-3TC-LPV/r usage (AOR 2.34; 95% CI: 1.40–3.90) and longer treatment duration (AOR 2.09; 95% CI: 1.30–3.34) were advantageous, good adherence significantly improved the likelihood of suppression (AOR 5.54; 95% CI: 3.27–9.38). Missed appointments and a bedridden state decreased the likelihood of suppression. Conclusions: HIV viral suppression was achieved in only 34% of participants. Adherence, ART regimen, treatment duration, functional status, and retention in care were significant predictors. Strengthening adherence support, patient retention, optimized ART regimens, routine viral load monitoring, and targeted care for high-risk patients could improve treatment outcomes and help Ethiopia achieve UNAIDS viral suppression targets. Full article

To address the risks of cross-border transmission of pathogenic microorganisms posed by the failure or non-compliance of shipboard ballast water treatment systems, ports urgently require efficient and flexible emergency response solutions. This study presents a novel, containerized, integrated ship-to-shore emergency response system specifically designed for the rapid inactivation of pathogenic microorganisms in ballast water. The core innovation lies in the integration of a three-degree-of-freedom (3-DOF) hydraulic robotic arm, a vision and positioning system, and a dynamic inflatable sealing structure designed for rapid, automated docking with a ship’s ballast water discharge outlet (DN250), thereby enhancing operational safety and efficiency. The system employs a purely physical treatment process of “ultrasound (US) pre-treatment + dual-stage ultraviolet (UV) disinfection,” allowing for reception and treatment without secondary chemical pollution. The integrated treatment train, consisting of US (30 kHz, 7.6–12 kW, minimum acoustic energy density ≥ 0.45 J/cm²) followed by dual-stage UV disinfection (minimum UV dose: 147 mJ/cm²), maintained effective microbial inactivation at turbidity levels of 15, 125, 250, and 500 NTU. US alone showed little direct bactericidal effect, whereas the first UV stage achieved log reduction values (LRVs) of 3.31–4.13, and the complete US + UV + UV process achieved total LRVs of 5.07–7.34 for Escherichia coli. The results showed that dual-stage UV disinfection was key to achieving high inactivation efficacy (p < 0.001), while ultrasound, despite its limited direct bactericidal effect, may have facilitated downstream UV disinfection within the sequential treatment train. This system not only fills a critical gap in port biosecurity emergency infrastructure but also provides an experimentally validated, efficient, environmentally friendly, and flexibly deployable shore-based solution. Full article